Yukon Flats National Wildlife Refuge

Fairbanks, AK, United States

Yukon Flats National Wildlife Refuge

Fairbanks, AK, United States
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Pastick N.J.,U.S. Geological Survey | Jorgenson M.T.,Alaska Ecoscience | Wylie B.K.,Eros | Minsley B.J.,U.S. Geological Survey | And 5 more authors.
Permafrost and Periglacial Processes | Year: 2013

Machine-learning regression tree models were used to extrapolate airborne electromagnetic resistivity data collected along flight lines in the Yukon Flats Ecoregion, central Alaska, for regional mapping of permafrost. This method of extrapolation (r=0.86) used subsurface resistivity, Landsat Thematic Mapper (TM) at-sensor reflectance, thermal, TM-derived spectral indices, digital elevation models and other relevant spatial data to estimate near-surface (0-2.6-m depth) resistivity at 30-m resolution. A piecewise regression model (r=0.82) and a presence/absence decision tree classification (accuracy of 87%) were used to estimate active-layer thickness (ALT) (< 101cm) and the probability of near-surface (up to 123-cm depth) permafrost occurrence from field data, modelled near-surface (0-2.6m) resistivity, and other relevant remote sensing and map data. At site scale, the predicted ALTs were similar to those previously observed for different vegetation types. At the landscape scale, the predicted ALTs tended to be thinner on higher-elevation loess deposits than on low-lying alluvial and sand sheet deposits of the Yukon Flats. The ALT and permafrost maps provide a baseline for future permafrost monitoring, serve as inputs for modelling hydrological and carbon cycles at local to regional scales, and offer insight into the ALT response to fire and thaw processes. Published 2013. This article is a U.S. Government work and is in the public domain in the USA.

Pastick N.J.,Stinger Ghaffarian Technologies Inc. | Jorgenson M.T.,Alaska Ecoscience | Wylie B.K.,U.S. Geological Survey | Rose J.R.,Yukon Flats National Wildlife Refuge | And 2 more authors.
Journal of Geophysical Research: Biogeosciences | Year: 2014

The distribution of permafrost is important to understand because of permafrost's influence on high-latitude ecosystem structure and functions. Moreover, near-surface (defined here as within 1-m of the Earth's surface) permafrost is particularly susceptible to a warming climate and is generally poorly mapped at regional scales. Subsequently, our objectives were to (1) develop the first-known binary and probabilistic maps of near-surface permafrost distributions at a 30 m resolution in the Alaskan Yukon River Basin by employing decision tree models, field measurements, and remotely sensed and mapped biophysical data; (2) evaluate the relative contribution of 39 biophysical variables used in the models; and (3) assess the landscape-scale factors controlling spatial variations in permafrost extent. Areas estimated to be present and absent of near-surface permafrost occupy approximately 46% and 45% of the Alaskan Yukon River Basin, respectively; masked areas (e.g., water and developed) account for the remaining 9% of the landscape. Strong predictors of near-surface permafrost include climatic indices, land cover, topography, and Landsat 7 Enhanced Thematic Mapper Plus spectral information. Our quantitative modeling approach enabled us to generate regional near-surface permafrost maps and provide essential information for resource managers and modelers to better understand near-surface permafrost distribution and how it relates to environmental factors and conditions. ©2014. American Geophysical Union. All Rights Reserved.

Pastick N.J.,U.S. Geological Survey | Rigge M.,U.S. Geological Survey | Wylie B.K.,U.S. Geological Survey | Jorgenson M.T.,Alaska Ecoscience | And 3 more authors.
Geoderma | Year: 2014

Understanding of the organic layer thickness (OLT) and organic layer carbon (OLC) stocks in subarctic ecosystems is critical due to their importance in the global carbon cycle. Moreover, post-fire OLT provides an indicator of long-term successional trajectories and permafrost susceptibility to thaw. To these ends, we 1) mapped OLT and associated uncertainty at 30m resolution in the Yukon River Basin (YRB), Alaska, employing decision tree models linking remotely sensed imagery with field and ancillary data, 2) converted OLT to OLC using a non-linear regression, 3) evaluate landscape controls on OLT and OLC, and 4) quantified the post-fire recovery of OLT and OLC. Areas of shallow (<10cm), moderate (≥10cm and <20cm), moderately thick (≥20cm and <30cm), and thick (≥30cm) OLT, composed 34, 20, 14, and 18% of the YRB, respectively; the average OLT was 19.4cm. Total OLC was estimated to be 3.38Pg. A regional chronosequence analysis over 30years revealed that OLT and OLC increased with stand age (OLT: R2=0.68; OLC: R2=0.66), where an average of 16cm OLT and 5.3kg/m2 OLC were consumed by fires. Strong predictors of OLT included climate, topography, near-surface permafrost distributions, soil wetness, and spectral information. Our modeling approach enabled us to produce regional maps of OLT and OLC, which will be useful in understanding risks and feedbacks associated with fires and climate feedbacks. © 2014 Elsevier B.V.

Anderson L.,U.S. Geological Survey | Birks J.,Alberta Innovates Technology Futures | Rover J.,U.S. Geological Survey | Guldager N.,Yukon Flats National Wildlife Refuge
Geophysical Research Letters | Year: 2013

High-latitude lakes are important for terrestrial carbon dynamics and waterfowl habitat driving a need to better understand controls on lake area changes. To identify the existence and cause of recent lake area changes in the Yukon Flats, a region of discontinuous permafrost in north central Alaska, we evaluate remotely sensed imagery with lake water isotope compositions and hydroclimatic parameters. Isotope compositions indicate that mixtures of precipitation, river water, and groundwater source ~95% of the studied lakes. The remaining minority are more dominantly sourced by snowmelt and/or permafrost thaw. Isotope-based water balance estimates indicate 58% of lakes lose more than half of inflow by evaporation. For 26% of the lakes studied, evaporative losses exceeded supply. Surface area trend analysis indicates that most lakes were near their maximum extent in the early 1980s during a relatively cool and wet period. Subsequent reductions can be explained by moisture deficits and greater evaporation. Key Points Isotopes of lake water identify net water balance and dominant water sources Most Yukon Flats lakes have evaporation losses that exceed 50% of inflow Recent lake reductions can be explained by hydroclimatic variations ©2013. American Geophysical Union. All Rights Reserved.

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